BudgetMAX

Inspiration

Think about this: you go out every week and tap your credit card many times for various purchases. You try to think about how much you spend and save your receipts, but ultimately lose them in the depths of your wallet. At the end of the month you see your credit bill and have a shocking realization that you are in debt. For years this has been the reality for college students and young people like us. In fact, a recent survey showed 79% of Canadians have low confidence in their finances. We wanted to create an app that will help users keep track of their spending by various categories so they can know where to make cuts.

What it does

We have a user interface that creates an individual database of how much they spend per week and what they spend it on. This information is used in pie graphs that clearly illustrate what their expenditures are. We also created a database that stores the information of all the users, tracking them by their IDs, classifying each account as an object.

How we built it

We used a Python framework that read inputs from CSV databases to build this code.

Challenges we ran into

As none of us had web development experience, it was somewhat tricky to build this framework from scratch using Python. We tried to incorporate machine learning in the code to actually give tips on the code, but we were unable to find large enough datasets to use machine learning on.

Accomplishments that we're proud of

We were able to maintain large databases with different user IDs based, employing objected oriented programming.

What we learned

We learned how to effectively use Matplotlib to plot charts in Python, and store and retrieve databases. Given this was all of our first Hackathon, we gained a large amount insight on how to extend web development using tools like Firebase, JavaScript and Django.

What's next for BudgetMax

We are interested in connecting Django with this to develop a more interactive front end. We are also interested in gathering a lot of data and implementing a machine learning model to suggest cuts for the user.

Built With

• anaconda
• jupyter-notebook
• mobirise
• python